---
title: Making Predictions using ML Models
sidebarTitle: Find()
---

## The `find()` Method

### Description

The `find()` method is used to get predictions from the model table. The data is
not persistent - it is returned on the fly as a result-document.

### Syntax

Here is the syntax:

```sql
db.predictor_name.find({column: "value", column: "value"});
```

On execution, we get:

```json
{
    "column_name1" : "value",
    "column_name2": "value",
    ...columns
    "select_data_query": null,
    "when_data": null,
    "target_name_original": "value",
    "target_name_confidence": "value",
    "target_name_explain": "{\"predicted_value\": value, \"confidence\": value, \"anomaly\": null, \"truth\": null, \"confidence_lower_bound\": value \"confidence_upper_bound\": value}",
    "target_name_anomaly": "value",
    "target_name_min": "value",
    "target_name_max": "value"
}
```

Where:

| Expressions                | Description                                                                                                                                                        |
| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `"target_name_original"`   | The real value of the target variable from the collection.                                                                                                         |
| `"target_name_confidence"` | Model confidence.                                                                                                                                                  |
| `"target_name_explain"`    | JSON object that contains additional information, such as `predicted_value`, `confidence`, `anomaly`, `truth`, `confidence_lower_bound`, `confidence_upper_bound`. |
| `"target_name_anomaly"`    | Model anomaly.                                                                                                                                                     |
| `"target_name_min"`        | Lower bound value.                                                                                                                                                 |
| `"target_name_max"`        | Upper bound value.                                                                                                                                                 |

## Example

### Making a Single Prediction

The following MQL statement fetches the predicted value of the `rental_price`
column from the `home_rentals_model` model. The predicted value is the rental
price of a property with attributes listed as a parameter to the `find()`
method.

```sql
db.home_rentals_model.find({sqft: "823", location: "good", neighborhood: "downtown", days_on_market: "10"});
```

On execution, we get:

```json
{
  "sqft": 823,
  "location": "good",
  "neighborhood": "downtown",
  "days_on_market": 10,
  "number_of_rooms": null,
  "number_of_bathrooms": null,
  "initial_price": null,
  "rental_price": 1431.323795180614,
  "select_data_query": null,
  "when_data": null,
  "rental_price_original": null,
  "rental_price_confidence": 0.99,
  "rental_price_explain": "{\"predicted_value\": 1431.323795180614, \"confidence\": 0.99, \"anomaly\": null, \"truth\": null, \"confidence_lower_bound\": 1379.4387560440227, \"confidence_upper_bound\": 1483.2088343172054}",
  "rental_price_anomaly": null,
  "rental_price_min": 1379.4387560440227,
  "rental_price_max": 1483.2088343172054
}
```

### Making Bulk Predictions

<Warning> 
**Bulk Predictions WIP**

The bulk predictions is a work in progress.

</Warning>
